# nolint start: commented_code_linter, line_length_linter,object_usage_linter.
library(here)
library(sf)
library(terra)
library(tidyverse)
library(lubridate)
library(exactextractr)
library(readxl)


# Vang alle command line argumenten op
args <- commandArgs(trailingOnly = TRUE)

# Controleer of er ten minste één argument is doorgegeven
if (length(args) == 0) {
  stop("Geen argumenten doorgegeven aan het script")
}

# Converteer het eerste argument naar een numerieke waarde
end_date <- as.Date(args[1])


offset <- as.numeric(args[2])
# Controleer of weeks_ago een geldig getal is
if (is.na(offset)) {
  # stop("Het argument is geen geldig getal") 
  offset <- 7
}

# Converteer het tweede argument naar een string waarde
project_dir <- as.character(args[3])

# Controleer of data_dir een geldige waarde is
if (!is.character(project_dir)) {
  project_dir <- "chemba"
}


laravel_storage_dir <- here("laravel_app/storage/app", project_dir)
#preparing directories
planet_tif_folder <- here(laravel_storage_dir, "merged_tif")
merged_final <- here(laravel_storage_dir, "merged_final_tif")

new_project_question = FALSE
planet_tif_folder <- here(laravel_storage_dir, "merged_tif")
merged_final <- here(laravel_storage_dir, "merged_final_tif")

data_dir <- here(laravel_storage_dir, "Data")
extracted_CI_dir <- here(data_dir, "extracted_ci")
daily_CI_vals_dir <- here(extracted_CI_dir, "daily_vals")
cumulative_CI_vals_dir <- here(extracted_CI_dir, "cumulative_vals")

weekly_CI_mosaic <- here(laravel_storage_dir, "weekly_mosaic")
daily_vrt <- here(data_dir, "vrt")
harvest_dir <-  here(data_dir, "HarvestData")

source("parameters_project.R")
source("ci_extraction_utils.R")
source("mosaic_creation_utils.R")

dir.create(here(laravel_storage_dir))
dir.create(here(data_dir))
dir.create(here(extracted_CI_dir))
dir.create(here(daily_CI_vals_dir))
dir.create(here(cumulative_CI_vals_dir))
dir.create(here(weekly_CI_mosaic))
dir.create(here(daily_vrt))
dir.create(merged_final)
dir.create(harvest_dir)

# end_date <- lubridate::dmy("20-6-2024")
week <- week(end_date)


#weeks_ago = 0
# Creating weekly mosaic
#dates <- date_list(weeks_ago)
dates <- date_list(end_date, offset)
print(dates)
#load pivot geojson
# pivot_sf_q <- st_read(here(data_dir, "pivot.geojson")) %>% dplyr::select(pivot, pivot_quadrant) %>% vect()

# raster_files <- list.files(planet_tif_folder,full.names = T, pattern = ".tif")

# filtered_files  <- map(dates$days_filter, ~ raster_files[grepl(pattern = .x, x = raster_files)]) %>%
#   compact() %>%
#   flatten_chr()
# head(filtered_files)

raster_files <- list.files(planet_tif_folder,full.names = T, pattern = ".tif")

filtered_files  <- map(dates$days_filter, ~ raster_files[grepl(pattern = .x, x = raster_files)]) %>%
  compact() %>%
  flatten_chr()
head(filtered_files)
# filtered_files <- raster_files #for first CI extraction

# create_mask_and_crop <- function(file, field_boundaries) {
# message("starting ", file)
#   CI <- rast(file)
# #   names(CI) <- c("green","nir")
# message("raster loaded")
#
#   CI <- CI[[2]]/CI[[1]]-1
# #   CI <- CI$nir/CI$green-1
# message("CI calculated")
#   CI <- terra::crop(CI, field_boundaries, mask = TRUE) #%>% CI_func()
#
#   new_file <- here(merged_final, paste0(tools::file_path_sans_ext(basename(file)), ".tif"))
#   writeRaster(CI, new_file, overwrite = TRUE)
#
#   vrt_file <- here(daily_vrt, paste0(tools::file_path_sans_ext(basename(file)), ".vrt"))
#   terra::vrt(new_file, vrt_file, overwrite = TRUE)
#
#   return(CI)
# }


vrt_list <- list()

# for (file in raster_files) {
#   v_crop <- create_mask_and_crop(file, field_boundaries)
#   message(file, " processed")
#   gc()
# }

for (file in filtered_files) {
  v_crop <- create_mask_and_crop(file, field_boundaries)
  emtpy_or_full <- global(v_crop, "notNA")

  vrt_file <- here(daily_vrt, paste0(tools::file_path_sans_ext(basename(file)), ".vrt"))
  if(emtpy_or_full[1,] >  100){
    vrt_list[vrt_file] <- vrt_file

  }else{
    file.remove(vrt_file)
  }

  message(file, " processed")
  gc()
}

# Extracting CI


# pivot_sf_q <- st_read(here("..", "Data", "pivot_20210625.geojson")) %>% dplyr::select(pivot, pivot_quadrant) %>% vect()
# pivot_sf <- st_read(here(data_dir, "pivot.geojson")) %>% dplyr::select(pivot, pivot_quadrant)   %>% group_by(pivot) %>% summarise() %>% vect()
# message("pivot loaded")

raster_files_NEW <- list.files(merged_final,full.names = T, pattern = ".tif")

# pivots_dates0 <- readRDS(here(harvest_dir, "harvest_data_new")) %>% filter(
#   pivot %in% c("1.1", "1.2", "1.3", "1.4", "1.6", "1.7", "1.8", "1.9", "1.10", "1.11", "1.12", "1.13",
#                "1.14" , "1.16" , "1.17" , "1.18" ,"2.1", "2.2", "2.3" , "2.4", "2.5", "3.1", "3.2", "3.3",
#                "4.1", "4.2", "4.3", "4.4", "4.5", "4.6", "5.1" ,"5.2", "5.3", "5.4", "6.1", "6.2", "DL1.1", "DL1.3")
# )

# harvesting_data <- pivots_dates0 %>%
#   select(c("pivot_quadrant", "season_start_2021", "season_end_2021", "season_start_2022", "season_end_2022", "season_start_2023", "season_end_2023", "season_start_2024", "season_end_2024")) %>%
#   pivot_longer(cols = starts_with("season"), names_to = "Year", values_to = "value") %>%
#   separate(Year, into = c("name", "Year"), sep = "(?<=season_start|season_end)\\_", remove = FALSE) %>%
#   mutate(name = str_to_title(name)) %>%
#   pivot_wider(names_from = name, values_from = value) %>%
#   rename(field = pivot_quadrant)

#If run for the firsttime, it will extract all data since the start and put into a table.rds. otherwise it will only add on to the existing table.


# Define the path to the file
file_path <- here(cumulative_CI_vals_dir,"combined_CI_data.rds")

# Check if the file exists
if (!file.exists(file_path)) {
  # Create the file with columns "column1" and "column2"
  data <- data.frame(sub_field=NA, field=NA)
  saveRDS(data, file_path)
}

print("combined_CI_data.rds exists, adding the latest image data to the table.")

filtered_files  <- map(dates$days_filter, ~ raster_files_NEW[grepl(pattern = .x, x = raster_files_NEW)]) %>%
  compact() %>%
  flatten_chr()
walk(filtered_files, extract_rasters_daily, field_geojson= field_boundaries, quadrants = TRUE, daily_CI_vals_dir)

extracted_values <-  list.files(daily_CI_vals_dir, full.names = TRUE)

extracted_values <- map(dates$days_filter, ~ extracted_values[grepl(pattern = .x, x = extracted_values)]) %>%
  compact() %>%
  flatten_chr()

pivot_stats <- extracted_values %>%
  map(readRDS) %>% list_rbind() %>%
  group_by(sub_field)


combined_CI_data <- readRDS(here(cumulative_CI_vals_dir,"combined_CI_data.rds")) #%>% drop_na(pivot_quadrant)
head(combined_CI_data)
pivot_stats2 <- bind_rows(pivot_stats, combined_CI_data)
# pivot_stats2 <- combined_CI_data
print("All CI values extracted from latest image.")
saveRDS(pivot_stats2, here(cumulative_CI_vals_dir,"combined_CI_data.rds")) #used to save the rest of the data into one file